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BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data

Fuente: arXiv

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Bibliographic Details
Main Authors:	Jumelet, Jaap, Fourtassi, Abdellah, Haga, Akari, Bunzeck, Bastian, Shandilya, Bhargav, Galvan-Sosa, Diana, Haznitrama, Faiz Ghifari, Padovani, Francesca, Meyer, Francois, Hu, Hai, Etxaniz, Julen, Prévot, Laurent, He, Linyang, Grandury, María, Marcheva, Mila, Foroutan, Negar, Theodoropoulos, Nikitas, Sadeghi, Pouya, Song, Siyuan, Salhan, Suchir, Zhou, Susana, Paniv, Yurii, Zhang, Ziyin, Bisazza, Arianna, Warstadt, Alex, Choshen, Leshem
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
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_version_	1866912642633826304
author	Jumelet, Jaap Fourtassi, Abdellah Haga, Akari Bunzeck, Bastian Shandilya, Bhargav Galvan-Sosa, Diana Haznitrama, Faiz Ghifari Padovani, Francesca Meyer, Francois Hu, Hai Etxaniz, Julen Prévot, Laurent He, Linyang Grandury, María Marcheva, Mila Foroutan, Negar Theodoropoulos, Nikitas Sadeghi, Pouya Song, Siyuan Salhan, Suchir Zhou, Susana Paniv, Yurii Zhang, Ziyin Bisazza, Arianna Warstadt, Alex Choshen, Leshem
author_facet	Jumelet, Jaap Fourtassi, Abdellah Haga, Akari Bunzeck, Bastian Shandilya, Bhargav Galvan-Sosa, Diana Haznitrama, Faiz Ghifari Padovani, Francesca Meyer, Francois Hu, Hai Etxaniz, Julen Prévot, Laurent He, Linyang Grandury, María Marcheva, Mila Foroutan, Negar Theodoropoulos, Nikitas Sadeghi, Pouya Song, Siyuan Salhan, Suchir Zhou, Susana Paniv, Yurii Zhang, Ziyin Bisazza, Arianna Warstadt, Alex Choshen, Leshem
contents	We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally plausible pretraining data aiming to cover the equivalent of 100M English words of content in each of 45 languages. We compile evaluation suites and train baseline models in each language. BabyBabelLM aims to facilitate multilingual pretraining and cognitive modeling.
format	Preprint
id	arxiv_https___arxiv_org_abs_2510_10159
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data Jumelet, Jaap Fourtassi, Abdellah Haga, Akari Bunzeck, Bastian Shandilya, Bhargav Galvan-Sosa, Diana Haznitrama, Faiz Ghifari Padovani, Francesca Meyer, Francois Hu, Hai Etxaniz, Julen Prévot, Laurent He, Linyang Grandury, María Marcheva, Mila Foroutan, Negar Theodoropoulos, Nikitas Sadeghi, Pouya Song, Siyuan Salhan, Suchir Zhou, Susana Paniv, Yurii Zhang, Ziyin Bisazza, Arianna Warstadt, Alex Choshen, Leshem Computation and Language We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally plausible pretraining data aiming to cover the equivalent of 100M English words of content in each of 45 languages. We compile evaluation suites and train baseline models in each language. BabyBabelLM aims to facilitate multilingual pretraining and cognitive modeling.
title	BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
topic	Computation and Language
url	https://arxiv.org/abs/2510.10159